Principal Components of the Meaning
Computation and Language
2020-09-21 v1 Machine Learning
Abstract
In this paper we argue that (lexical) meaning in science can be represented in a 13 dimension Meaning Space. This space is constructed using principal component analysis (singular decomposition) on the matrix of word category relative information gains, where the categories are those used by the Web of Science, and the words are taken from a reduced word set from texts in the Web of Science. We show that this reduced word set plausibly represents all texts in the corpus, so that the principal component analysis has some objective meaning with respect to the corpus. We argue that 13 dimensions is adequate to describe the meaning of scientific texts, and hypothesise about the qualitative meaning of the principal components.
Keywords
Cite
@article{arxiv.2009.08859,
title = {Principal Components of the Meaning},
author = {Neslihan Suzen and Alexander Gorban and Jeremy Levesley and Evgeny Mirkes},
journal= {arXiv preprint arXiv:2009.08859},
year = {2020}
}